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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Aug 2017
Next issue: Nov 2017
Avg review time: 78 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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ABC Algorithm based Fuzzy Modeling of Optical Glucose Detection, SARACOGLU, O. G., BAGIS, A., KONAR, M., TABARU, T. E.
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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
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2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  3/2016 - 4

Thermal Response Estimation in Substation Connectors Using Data-Driven Models

GIACOMETTO, F. See more information about GIACOMETTO, F. on SCOPUS See more information about GIACOMETTO, F. on IEEExplore See more information about GIACOMETTO, F. on Web of Science, CAPELLI, F. See more information about  CAPELLI, F. on SCOPUS See more information about  CAPELLI, F. on SCOPUS See more information about CAPELLI, F. on Web of Science, ROMERAL, L. See more information about  ROMERAL, L. on SCOPUS See more information about  ROMERAL, L. on SCOPUS See more information about ROMERAL, L. on Web of Science, RIBA, J.-R. See more information about  RIBA, J.-R. on SCOPUS See more information about  RIBA, J.-R. on SCOPUS See more information about RIBA, J.-R. on Web of Science, SALA, E. See more information about SALA, E. on SCOPUS See more information about SALA, E. on SCOPUS See more information about SALA, E. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,247 KB) | Citation | Downloads: 203 | Views: 505

Author keywords
computer simulation, connectors, finite element methods, predictive models, thermal analysis

References keywords
engineer(8), comput(7), neural(6), jcie(6), indust(6), simulation(5), process(5), finite(5), element(5), time(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2016-08-31
Volume 16, Issue 3, Year 2016, On page(s): 25 - 30
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.03004
Web of Science Accession Number: 000384750000004
SCOPUS ID: 84991096061

Abstract
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Temperature rise simulations are one of the key steps in the design of high-voltage substation connectors. These simulations help minimizing the number of experimental tests, which are power consuming and expensive. The conventional approach to perform these simulations relies on finite element method (FEM). It is highly desirable to reduce the number of required FEM simulations since they are time-consuming. To this end, this paper presents a data-driven modeling approach to drastically shorten the required simulation time. The data-driven approach estimates the thermal response of substation connectors from the data provided by a reduced number of FEM simulations of different operating conditions, thus allowing extrapolating the thermal response to other operating conditions. In the study, a partitioning method is also applied to enhance the performance of the learning stage of a set of data-driven methods, which are then compared and evaluated in terms of simulation time and accuracy to select the optimal configuration of the data-driven model. Finally, the complete methodology is validated against simulation tests.


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Copyright ©2001-2017
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

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